Data Science For Dummies

MONETIZE YOUR COMPANY’S DATA AND DATA SCIENCE EXPERTISE WITHOUT SPENDING A FORTUNE ON HIRING INDEPENDENT STRATEGY CONSULTANTS TO HELP

What if there was one simple, clear process for ensuring that all your company’s data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that’s most prime for achieving profitability while also moving your company closer to its business vision? There is.

INDUSTRY-ACCLAIMED DATA SCIENCE CONSULTANT, LILLIAN PIERSON, SHARES HER PROPRIETARY STAR FRAMEWORK – A SIMPLE, PROVEN PROCESS FOR LEADING PROFIT-FORMING DATA SCIENCE PROJECTS.

Not sure what data science is yet? Don’t worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you’re already a data science expert? Then you really won’t want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book.

Data Science For Dummies demonstrates:

* The only process you’ll ever need to lead profitable data science projects
* Secret, reverse-engineered data monetization tactics that no one’s talking about
* The shocking truth about how simple natural language processing can be
* How to beat the crowd of data professionals by cultivating your own unique blend of data science expertise

Whether you’re new to the data science field or already a decade in, you’re sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company’s data by picking up your copy today.

LILLIAN PIERSON is the CEO of Data-Mania, where she supports data professionals in transforming into world-class leaders and entrepreneurs. She has trained well over one million individuals on the topics of AI and data science. Lillian has assisted global leaders in IT, government, media organizations, and nonprofits.

INTRODUCTION 1

About This Book 3

Foolish Assumptions 3

Icons Used in This Book 4

Beyond the Book 4

Where to Go from Here 4

PART 1: GETTING STARTED WITH DATA SCIENCE 5

CHAPTER 1: WRAPPING YOUR HEAD AROUND DATA SCIENCE 7

Seeing Who Can Make Use of Data Science 8

Inspecting the Pieces of the Data Science Puzzle 10

Collecting, querying, and consuming data 11

Applying mathematical modeling to data science tasks 12

Deriving insights from statistical methods 12

Coding, coding, coding — it’s just part of the game 13

Applying data science to a subject area 13

Communicating data insights 14

Exploring Career Alternatives That Involve Data Science 15

The data implementer 16

The data leader 16

The data entrepreneur 17

CHAPTER 2: TAPPING INTO CRITICAL ASPECTS OF DATA ENGINEERING 19

Defining Big Data and the Three Vs 19

Grappling with data volume 21

Handling data velocity 21

Dealing with data variety 22

Identifying Important Data Sources 23

Grasping the Differences among Data Approaches 24

Defining data science 25

Defining machine learning engineering 26

Defining data engineering 26

Comparing machine learning engineers, data scientists, and data engineers 27

Storing and Processing Data for Data Science 28

Storing data and doing data science directly in the cloud 28

Storing big data on-premise 32

Processing big data in real-time 35

PART 2: USING DATA SCIENCE TO EXTRACT MEANING FROM YOUR DATA 37

CHAPTER 3: MACHINE LEARNING MEANS USING A MACHINE TO LEARN FROM DATA 39

Defining Machine Learning and Its Processes 40

Walking through the steps of the machine learning process 40

Becoming familiar with machine learning terms 41

Considering Learning Styles 42

Learning with supervised algorithms 42

Learning with unsupervised algorithms 43

Learning with reinforcement 43

Seeing What You Can Do 43

Selecting algorithms based on function 44

Using Spark to generate real-time big data analytics 48

CHAPTER 4: MATH, PROBABILITY, AND STATISTICAL MODELING 51

Exploring Probability and Inferential Statistics 52

Probability distributions 53

Conditional probability with Naïve Bayes 55

Quantifying Correlation 56

Calculating correlation with Pearson’s r 56

Ranking variable-pairs using Spearman’s rank correlation 58

Reducing Data Dimensionality with Linear Algebra 59

Decomposing data to reduce dimensionality 59

Reducing dimensionality with factor analysis 63

Decreasing dimensionality and removing outliers with PCA 64

Modeling Decisions with Multiple Criteria Decision-Making 65

Turning to traditional MCDM 65

Focusing on fuzzy MCDM 67

Introducing Regression Methods 67

Linear regression 67

Logistic regression 69

Ordinary least squares (OLS) regression methods 70

Detecting Outliers 70

Analyzing extreme values 70

Detecting outliers with univariate analysis 71

Detecting outliers with multivariate analysis 73

Introducing Time Series Analysis 73

Identifying patterns in time series 74

Modeling univariate time series data 75

CHAPTER 5: GROUPING YOUR WAY INTO ACCURATE PREDICTIONS 77

Starting with Clustering Basics 78

Getting to know clustering algorithms 79

Examining clustering similarity metrics 81

Identifying Clusters in Your Data 82

Clustering with the k-means algorithm 82

Estimating clusters with kernel density estimation (KDE) 84

Clustering with hierarchical algorithms 84

Dabbling in the DBScan neighborhood 87

Categorizing Data with Decision Tree and Random Forest Algorithms 88

Drawing a Line between Clustering and Classification 89

Introducing instance-based learning classifiers 90

Getting to know classification algorithms 90

Making Sense of Data with Nearest Neighbor Analysis 93

Classifying Data with Average Nearest Neighbor Algorithms 94

Classifying with K-Nearest Neighbor Algorithms 97

Understanding how the k-nearest neighbor algorithm works 98

Knowing when to use the k-nearest neighbor algorithm 99

Exploring common applications of k-nearest neighbour algorithms 100

Solving Real-World Problems with Nearest Neighbor Algorithms 100

Seeing k-nearest neighbor algorithms in action 101

Seeing average nearest neighbor algorithms in action 101

CHAPTER 6: CODING UP DATA INSIGHTS AND DECISION ENGINES 103

Seeing Where Python and R Fit into Your Data Science Strategy 104

Using Python for Data Science 104

Sorting out the various Python data types 106

Putting loops to good use in Python 109

Having fun with functions 110

Keeping cool with classes 112

Checking out some useful Python libraries 114

Using Open Source R for Data Science 120

Comprehending R’s basic vocabulary 121

Delving into functions and operators 124

Iterating in R 127

Observing how objects work 129

Sorting out R’s popular statistical analysis packages 131

Examining packages for visualizing, mapping, and graphing in R 133

CHAPTER 7: GENERATING INSIGHTS WITH SOFTWARE APPLICATIONS 137

Choosing the Best Tools for Your Data Science Strategy 138

Getting a Handle on SQL and Relational Databases 139

Investing Some Effort into Database Design 144

Defining data types 144

Designing constraints properly 145

Normalizing your database 145

Narrowing the Focus with SQL Functions 147

Making Life Easier with Excel 151

Using Excel to quickly get to know your data 152

Reformatting and summarizing with PivotTables 157

Automating Excel tasks with macros 158

CHAPTER 8: TELLING POWERFUL STORIES WITH DATA 161

Data Visualizations: The Big Three 162

Data storytelling for decision makers 162

Data showcasing for analysts 163

Designing data art for activists 164

Designing to Meet the Needs of Your Target Audience 164

Step 1: Brainstorm (All about Eve) 165

Step 2: Define the purpose 166

Step 3: Choose the most functional visualization type for your purpose 166

Picking the Most Appropriate Design Style 167

Inducing a calculating, exacting response 167

Eliciting a strong emotional response 168

Selecting the Appropriate Data Graphic Type 170

Standard chart graphics 171

Comparative graphics 173

Statistical plots 176

Topology structures 179

Spatial plots and maps 180

Testing Data Graphics 183

Adding Context 184

Creating context with data 184

Creating context with annotations 185

Creating context with graphical elements 186

PART 3: TAKING STOCK OF YOUR DATA SCIENCE CAPABILITIES 187

CHAPTER 9: DEVELOPING YOUR BUSINESS ACUMEN 189

Bridging the Business Gap 189

Contrasting business acumen with subject matter expertise 190

Defining business acumen 191

Traversing the Business Landscape 192

Seeing how data roles support the business in making money 192

Leveling up your business acumen 195

Fortifying your leadership skills 196

Surveying Use Cases and Case Studies 197

Documentation for data leaders 199

Documentation for data implementers 202

CHAPTER 10: IMPROVING OPERATIONS 205

Establishing Essential Context for Operational Improvements Use Cases 206

Exploring Ways That Data Science Is Used to Improve Operations 207

Making major improvements to traditional manufacturing operations 208

Optimizing business operations with data science 210

An AI case study: Automated, personalized, and effective debt collection processes 211

Gaining logistical efficiencies with better use of real-time data 216

Another AI case study: Real-time optimized logistics routing 217

Modernizing media and the press with data science and AI 222

Generating content with the click of a button 222

Yet another case study: Increasing content generation rates 224

CHAPTER 11: MAKING MARKETING IMPROVEMENTS 229

Exploring Popular Use Cases for Data Science in Marketing 229

Turning Web Analytics into Dollars and Sense 232

Getting acquainted with omnichannel analytics 233

Mapping your channels 233

Building analytics around channel performance 235

Scoring your company’s channels 235

Building Data Products That Increase Sales-and-Marketing ROI 238

Increasing Profit Margins with Marketing Mix Modeling 239

Collecting data on the four Ps 240

Implementing marketing mix modeling 241

Increasing profitability with MMM 243

CHAPTER 12: ENABLING IMPROVED DECISION-MAKING 245

Improving Decision-Making 245

Barking Up the Business Intelligence Tree 247

Using Data Analytics to Support Decision-Making 249

Types of analytics 252

Common challenges in analytics 252

Data wrangling 253

Increasing Profit Margins with Data Science 254

Seeing which kinds of data are useful when using data science for decision support 255

Directing improved decision-making for call center agents 257

Discovering the tipping point where the old way stops working 262

CHAPTER 13: DECREASING LENDING RISK AND FIGHTING FINANCIAL CRIMES 265

Decreasing Lending Risk with Clustering and Classification 266

Preventing Fraud Via Natural Language Processing (NLP) 267

CHAPTER 14: MONETIZING DATA AND DATA SCIENCE EXPERTISE 275

Setting the Tone for Data Monetization 275

Monetizing Data Science Skills as a Service 278

Data preparation services 279

Model building services 280

Selling Data Products 282

Direct Monetization of Data Resources 283

Coupling data resources with a service and selling it 283

Making money with data partnerships 284

Pricing Out Data Privacy 285

PART 4: ASSESSING YOUR DATA SCIENCE OPTIONS 289

CHAPTER 15: GATHERING IMPORTANT INFORMATION ABOUT YOUR COMPANY 291

Unifying Your Data Science Team Under a Single Business Vision 292

Framing Data Science around the Company’s Vision, Mission, and Values 294

Taking Stock of Data Technologies 296

Inventorying Your Company’s Data Resources 298

Requesting your data dictionary and inventory 298

Confirming what’s officially on file 300

Unearthing data silos and data quality issues 300

People-Mapping 303

Requesting organizational charts 303

Surveying the skillsets of relevant personnel 304

Avoiding Classic Data Science Project Pitfalls 305

Staying focused on the business, not on the tech 305

Drafting best practices to protect your data science project 306

Tuning In to Your Company’s Data Ethos 306

Collecting the official data privacy policy 307

Taking AI ethics into account 307

Making Information-Gathering Efficient 308

CHAPTER 16: NARROWING IN ON THE OPTIMAL DATA SCIENCE USE CASE 311

Reviewing the Documentation 312

Selecting Your Quick-Win Data Science Use Cases 313

Zeroing in on the quick win 313

Producing a POTI model 314

Picking between Plug-and-Play Assessments 316

Carrying out a data skill gap analysis for your company 317

Assessing the ethics of your company’s AI projects and products 318

Assessing data governance and data privacy policies 323

CHAPTER 17: PLANNING FOR FUTURE DATA SCIENCE PROJECT SUCCESS 327

Preparing an Implementation Plan 328

Supporting Your Data Science Project Plan 335

Analyzing your alternatives 335

Interviewing intended users and designing accordingly 337

POTI modeling the future state 338

Executing On Your Data Science Project Plan 339

CHAPTER 18: BLAZING A PATH TO DATA SCIENCE CAREER SUCCESS 341

Navigating the Data Science Career Matrix 341

Landing Your Data Scientist Dream Job 343

Leaning into data science implementation 345

Acing your accreditations 346

Making the grade with coding bootcamps and data science career accelerators 348

Networking and building authentic relationships 349

Developing your own thought leadership in data science 350

Building a public data science project portfolio 351

Leading with Data Science 354

Starting Up in Data Science 357

Choosing a business model for your data science business 357

Selecting a data science start-up revenue model 359

Taking inspiration from Kam Lee’s success story 361

Following in the footsteps of the data science entrepreneurs 364

PART 5: THE PART OF TENS 367

CHAPTER 19: TEN PHENOMENAL RESOURCES FOR OPEN DATA 369

Digging Through data.gov 370

Checking Out Canada Open Data 371

Diving into data.gov.uk 372

Checking Out US Census Bureau Data 373

Accessing NASA Data 374

Wrangling World Bank Data 375

Getting to Know Knoema Data 376

Queuing Up with Quandl Data 378

Exploring Exversion Data 379

Mapping OpenStreetMap Spatial Data 380

CHAPTER 20: TEN FREE OR LOW-COST DATA SCIENCE TOOLS AND APPLICATIONS 381

Scraping, Collecting, and Handling Data Tools 382

Sourcing and aggregating image data with ImageQuilts 382

Wrangling data with DataWrangler 383

Data-Exploration Tools 384

Getting up to speed in Gephi 384

Machine learning with the WEKA suite 386

Designing Data Visualizations 387

Getting Shiny by RStudio 387

Mapmaking and spatial data analytics with CARTO 388

Talking about Tableau Public 390

Using RAWGraphs for web-based data visualization 392

Communicating with Infographics 393

Making cool infographics with Infogram 394

Making cool infographics with Piktochart 395

Index 397